1. Field of Invention
The present invention relates to a system and a method for virtual metrology (VM). More particularly, the present invention relates to a system and a method for automatic virtual metrology (AVM) which can fan out or port a full set of VM models of a certain type of equipment to other equipment of the same type with ensuring VM accuracy.
2. Description of Related Art
In the semiconductor and TFT-LCD industries, to ensure process stability and improve yield rate of production equipment, it is necessary to conduct on-line quality monitoring on each workpiece (i.e. “wafer” in IC foundries or “glass” in TFT-LCD factories) processed on the production equipment. Virtual metrology is used to conjecture the quality of a workpiece fabricated by a piece of production equipment by using the process data collected from production equipment, when physical metrology is either impossible or unavailable to be conducted on the workpiece. When virtual metrology is applied, since the physical features of process chambers in the same equipment or the same type of equipment are not quite the same, the conjecture models of the respective process chambers have to be built in accordance with their own features, so as to maintain the conjecturing accuracy of virtual metrology. Hence, when it is desired to implement virtual metrology fab-wide, a conventional skill needs to construct a conjecture model for each process chamber (apparatus) of each equipment, and thus the amount of the prediction models in the whole plant is becoming enormous when the equipment types and numbers increase. When the conventional skill which needs to create individual models for the respective process chambers of each equipment is applied, a large amount of historical data has to be extracted for creating those models one by one, thus consuming a lot of manpower and cost, so that implementing virtual metrology on the whole plant becomes nearly impossible. Hence, there is a need to develop a system and a method for automatic virtual metrology (AVM) to overcome the aforementioned problems.
Further, the conventional skill does not have the capability of performing online and real time quality evaluation on the collected process data or actual metrology data. Therefore, if the collected process data or actual metrology data are abnormal, the conventional skill will still use the abnormal data to tune or re-train the virtual metrology models, thus affecting the conjecturing accuracy of virtual metrology. Moreover, in order to overcome the difficulty of automatically evaluating and sifting a large amount of data needed for implementing the virtual metrology on all the process apparatuses (such as process chambers) of each equipment of the whole plant, the conventional skill have to own the capability of automatically performing online and real-time data quality evaluation on the collected process data and actual metrology data, thereby automatically excluding abnormal process data and actual metrology data, thus saving a lot of manpower and time. Hence, there is also a need to develop data quality indexes having the capability of automatically evaluating and sifting data.